Human Mobility Mining Using Spatio-Temporal Data

Georuumilised tehnoloogiad on lahutamatu osa meie elust: tehnoloogilise arengu ja positsioneerimiseadmete levikuga on toimunud kiire kasv kättesaadavate georuumiliste andmete mahus. Andmed kogutakse erinevate allikate kaudu, nt GPS ja mobiilseadmete logid, traadita sidevahendid ja asukohapõhised teenused ning teised positsioneerimise süsteemid. Liikumise kohta on võimalik infot koguda suures mõõtkavas ja hea täpsusega - see annab uurijatele võimaluse luua uusi ja innovaatilisi platvorme ja teenuseid georuumilise info analüüsimiseks ning parandada andmete kaevandamise ja visualiseerimise tehnikaid. Selleks, et luua hea nõustamisssüsteem, on väga oluline saada aru inimeste liikumisharjumustest ja käitumisest ning leida igapäevaste tegevuste varjatud mustrid. Magistritöö eesmärgiks on analüüsida andmekaeve meetodeid, uurides, millised mustrid võivad olla liikumise trajektoorides või milliste algoritmidega saab ennustada inimeste käitumist. Töös kontrollitakse nii olemasolevaid metoodikad ja teooriad ruumilise andmekaevandamise valdkonnas kui ka pakutakse arendatud algoritmide jada inimeste liikumise ennustamiseks. Me hindame ja vördleme tulemusi omavahel ning töötame välja metoodika inimeste liikumiskäitumise adaptiivseks andmekaevandamiseks.Geospatial technologies have become an integral part of our lives. With technological progress and rapid increase of geospatial information and inexpensive positioning technologies, more space-related data is becoming available at any time. Data is collected using multiple sources such as GPS and mobile computer logs, wireless communication devices, location-aware services and other positioning systems. This gives scientists the opportunity to create new innovative platforms for spatio-temporal data analysis and improve methods for mining and visualization for decision support. In order to provide a good decision support systems, it is vital to understand people’s movement, mobility behaviour and be able to discover hidden patterns and associations in their daily activities. The aim of this thesis is to analyze and discuss spatial data mining techniques by answering questions like what kinds of patterns can be extracted from spatio-temporal data or which methods are best for predicting human mobility behavior. In this work, we verify existing methodologies and theories about spatio-temporal data mining and propose a sequence of algorithms to achieve good human mobility prediction. We evaluate the results and propose a methodology for adaptive data mining of human mobility behavior

Automated annotation of landmark images using community contributed datasets and web resources

A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching oers 93.6% classication accuracy; the selection of appropriate tags for use in annotation performs well (F1M of 0.59), and it subsequently automatically identies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Knowledge discovery from trajectories

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAs a newly proliferating study area, knowledge discovery from trajectories has attracted more and more researchers from different background. However, there is, until now, no theoretical framework for researchers gaining a systematic view of the researches going on. The complexity of spatial and temporal information along with their combination is producing numerous spatio-temporal patterns. In addition, it is very probable that a pattern may have different definition and mining methodology for researchers from different background, such as Geographic Information Science, Data Mining, Database, and Computational Geometry. How to systematically define these patterns, so that the whole community can make better use of previous research? This paper is trying to tackle with this challenge by three steps. First, the input trajectory data is classified; second, taxonomy of spatio-temporal patterns is developed from data mining point of view; lastly, the spatio-temporal patterns appeared on the previous publications are discussed and put into the theoretical framework. In this way, researchers can easily find needed methodology to mining specific pattern in this framework; also the algorithms needing to be developed can be identified for further research. Under the guidance of this framework, an application to a real data set from Starkey Project is performed. Two questions are answers by applying data mining algorithms. First is where the elks would like to stay in the whole range, and the second is whether there are corridors among these regions of interest